Skip header and navigation

Refine By

41 records – page 1 of 3.

Longitudinal Associations of Explosive and Adventurous Temperament Profiles With Character Development: The Modifying Effects of Social Support and Attachment.

https://arctichealth.org/en/permalink/ahliterature302895
Source
J Clin Psychiatry. 2018 Mar/Apr; 79(2):
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Author
Aino I L Saarinen
Tom H Rosenström
Christian A Hakulinen
Claude Robert Cloninger
Mirka H M Hintsanen
Laura M Pulkki-Råback
Terho Lehtimäki
Olli T Raitakari
Liisa Keltikangas-Järvinen
Author Affiliation
Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Source
J Clin Psychiatry. 2018 Mar/Apr; 79(2):
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Adult
Child
Child, Preschool
Female
Finland
Follow-Up Studies
Humans
Longitudinal Studies
Male
Middle Aged
Models, Statistical
Object Attachment
Personality - physiology
Registries
Social Class
Social Support
Temperament - physiology
Young Adult
Abstract
The aim of this study was to examine (a) whether adventurous and explosive temperament profiles (presumed precursors of antisocial and borderline personality) are associated with character traits over a 15-year follow-up and (b) whether social support and attachment security modify the relationship between temperament profiles and character development.
2,028 subjects of the Young Finns study completed the Temperament and Character Inventory, the Multidimensional Scale of Perceived Social Support, and the Relationship Questionnaire at 3 assessment points between 1997 and 2012.
Both explosive and adventurous temperament profiles seemed to predispose individuals to have less mature personalities; that is, these profiles were consistently associated with lower cooperativeness (P
PubMed ID
29469244 View in PubMed
Less detail

Genetic Factors Explain a Major Fraction of the 50% Lower Lipoprotein(a) Concentrations in Finns.

https://arctichealth.org/en/permalink/ahliterature301782
Source
Arterioscler Thromb Vasc Biol. 2018 05; 38(5):1230-1241
Publication Type
Comparative Study
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Date
05-2018
Author
Gertraud Erhart
Claudia Lamina
Terho Lehtimäki
Pedro Marques-Vidal
Mika Kähönen
Peter Vollenweider
Olli T Raitakari
Gérard Waeber
Barbara Thorand
Konstantin Strauch
Christian Gieger
Thomas Meitinger
Annette Peters
Florian Kronenberg
Stefan Coassin
Author Affiliation
From the Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Medical University of Innsbruck, Austria (G.E., C.L., F.K., S.C.).
Source
Arterioscler Thromb Vasc Biol. 2018 05; 38(5):1230-1241
Date
05-2018
Language
English
Publication Type
Comparative Study
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Keywords
Adult
Aged
Apolipoproteins E - genetics
European Continental Ancestry Group - genetics
Female
Finland - epidemiology
Gene Frequency
Genetics, Population
Haplotypes
Humans
Lipoprotein(a) - blood - genetics
Male
Middle Aged
Phenotype
Polymorphism, Single Nucleotide
Proprotein Convertase 9 - genetics
Protein Isoforms
Risk factors
Abstract
Lp(a) (lipoprotein(a)) concentrations are widely genetically determined by the LPA isoforms and show 5-fold interpopulation differences. Two- to 3-fold differences have been reported even within Europe. Finns represent a distinctive population isolate within Europe and have been repeatedly reported to present lower Lp(a) concentrations than Central Europeans. The significance of this finding was unclear for a long time because of the difficult comparability of Lp(a) assays. Recently, a large standardized study in >50?000 individuals from 7 European populations confirmed this observation but could not provide insights into the causes.
We investigated Lp(a) concentrations, LPA isoforms, and genotypes of established genetic variants affecting Lp(a) concentrations (LPA variants, APOE isoforms, and PCSK9 R46L) in the Finnish YFS (Cardiovascular Risk in Young Finns Study) population (n=2281) and 3 Non-Finnish Central European populations (n=10?003). We observed ˜50% lower Lp(a) concentrations in Finns. The isoform distribution was shifted toward longer isoforms, and the percentage of low-molecular-weight isoform carriers was reduced. Most interestingly, however, Lp(a) was reduced in each single-isoform group. In contrast to the known inverse relationship between LPA isoforms and Lp(a) concentrations, especially very short isoforms presented unexpectedly low Lp(a) concentrations in Finns. The investigated genetic variants, as well as age, sex, and renal function, explained 71.8% of the observed population differences.
The population differences in Lp(a) concentrations between Finnish and Central European populations originate not only from a different LPA isoform distribution but suggest the existence of novel functional variation in the small-isoform range.
PubMed ID
29567679 View in PubMed
Less detail

The relationship of dispositional compassion for others with depressive symptoms over a 15-year prospective follow-up.

https://arctichealth.org/en/permalink/ahliterature301574
Source
J Affect Disord. 2019 05 01; 250:354-362
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
05-01-2019
Author
Aino Saarinen
Liisa Keltikangas-Järvinen
C Robert Cloninger
Juha Veijola
Marko Elovainio
Terho Lehtimäki
Olli Raitakari
Mirka Hintsanen
Author Affiliation
Research Unit of Psychology, University of Oulu, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland.
Source
J Affect Disord. 2019 05 01; 250:354-362
Date
05-01-2019
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Depressive Disorder - psychology
Empathy
Female
Finland
Follow-Up Studies
Humans
Male
Prospective Studies
Psychiatric Status Rating Scales
Socioeconomic Factors
Temperament
Young Adult
Abstract
The aim of this study was to investigate (i) the direction of the relationships between dispositional compassion for others and depressive symptoms over a 15-year follow-up in adulthood and (ii) the longitudinal associations of dispositional compassion with total depressive symptoms and various depressive subsymptoms (i.e. negative attitude, performance difficulties, and somatic complaints) from early adulthood to middle age.
The participants (N?=?1676) came from the prospective Young Finns Study. Dispositional compassion was assessed with the Temperament and Character Inventory and depressive symptoms with a modified version of the Beck Depression Inventory. The measurements were conducted between 1997-2012 including three measurement points. The data was analyzed using structural equation models and multilevel models for longitudinal design.
The predictive relationships were more likely to proceed from high dispositional compassion for others to lower depressive symptoms than in the opposite direction. Additionally, high dispositional compassion predicted a lower total score of depressive symptoms and also lower scores of various depressive subsymptoms (negative attitude, performance difficulties, somatic complaints) in early adulthood. These associations, however, weakened over years and became non-significant in middle age. All the findings were sustained after controlling for age, gender, and socioeconomic factors in childhood and adulthood.
Depressive symptoms were mostly mild and non-clinical in our sample. The findings cannot be directly generalized to severe depressive symptomatology.
When tailoring psychiatric interventions, it is necessary to be aware that compassion for others may lower the risk for the onset and maintenance of depressive symptoms, especially in early adulthood.
PubMed ID
30877858 View in PubMed
Less detail

Both youth and long-term vitamin D status is associated with risk of type 2 diabetes mellitus in adulthood: a cohort study.

https://arctichealth.org/en/permalink/ahliterature301194
Source
Ann Med. 2018 02; 50(1):74-82
Publication Type
Comparative Study
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Date
02-2018
Author
Feitong Wu
Markus Juonala
Niina Pitkänen
Antti Jula
Terho Lehtimäki
Matthew A Sabin
Katja Pahkala
Nina Hutri-Kähönen
Mika Kähönen
Tomi Laitinen
Jorma S A Viikari
Costan G Magnussen
Olli T Raitakari
Author Affiliation
a Menzies Institute for Medical Research , University of Tasmania , Hobart , TAS , Australia.
Source
Ann Med. 2018 02; 50(1):74-82
Date
02-2018
Language
English
Publication Type
Comparative Study
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Child
Child, Preschool
Cohort Studies
Diabetes Mellitus, Type 2 - blood - epidemiology - etiology - prevention & control
Fasting
Female
Finland - epidemiology
Humans
Incidence
Male
Prediabetic State - blood - epidemiology
Risk factors
Vitamin D - analogs & derivatives - blood
Abstract
To determine whether vitamin D status in childhood and adolescence (herein collectively referred to as youth) and the long-term status from youth to adulthood is associated with risk of developing type 2 diabetes mellitus (T2DM) and impaired fasting glucose (IFG) in adulthood.
This was a 31-year follow-up study of 2300 participants aged 3-18 years. Multinomial logistic regression was used to assess the association of both (a) baseline 25-hydroxyvitamin D (25OHD) levels and (b) the mean of baseline and the latest follow-up 25OHD levels (continuous variable and quartiles) with incident T2DM and IFG (cut-off?=?5.6?mmol/L) in adult life.
High serum 25OHD levels in youth and also mean values from youth to adulthood were associated with reduced risk of developing T2DM in adulthood (odds ratio, 95% confidence interval=?0.73, 0.57-0.95 and 0.65, 0.51-0.84, respectively, for each SD increment in 25OHD). Compared to Q1, a dose-dependent negative association was observed across other quartiles of youth 25OHD, while the strongest association was found in the Q3 for the mean 25OHD levels. Neither youth nor the mean 25OHD was associated with IFG.
High serum 25OHD levels in youth, and from child to adult life, were associated with a reduced risk of developing T2DM in adulthood. Key Messages High serum 25OHD levels in youth, and between youth and adulthood, were associated with a lower risk of T2DM in adulthood. Each SD (15.2?nmol/L) increment in youth serum 25OHD levels was associated with a 26% reduction in odds for T2DM, which was independent of a number of confounding variables and other risk factors for T2DM. A similar magnitude of association was observed for the long-term 25OHD levels between youth and adulthood. These findings suggest a potentially simple and cost-effective strategy for reducing adulthood risk of T2DM starting in an earlier stage of life - improving and maintaining vitamin D status throughout youth and early adulthood.
PubMed ID
29113496 View in PubMed
Less detail

Does education protect against depression? Evidence from the Young Finns Study using Mendelian randomization.

https://arctichealth.org/en/permalink/ahliterature300937
Source
Prev Med. 2018 10; 115:134-139
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2018
Author
Jutta Viinikainen
Alex Bryson
Petri Böckerman
Marko Elovainio
Niina Pitkänen
Laura Pulkki-Råback
Terho Lehtimäki
Olli Raitakari
Jaakko Pehkonen
Author Affiliation
University of Jyväskylä, Jyväskylä University School of Business and Economics, Jyväskylä, Finland. Electronic address: jutta.viinikainen@jyu.fi.
Source
Prev Med. 2018 10; 115:134-139
Date
10-2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Depression - genetics
Educational Status
Female
Finland
Genome-Wide Association Study - methods
Humans
Male
Mendelian Randomization Analysis - methods
Middle Aged
Risk factors
Abstract
Using participants (N?=?1733) drawn from the nationally representative longitudinal Young Finns Study (YFS) we estimate the effect of education on depressive symptoms. In 2007, when the participants were between 30 and 45?years old, they reported their depressive symptoms using a revised version of Beck's Depression Inventory. Education was measured using register information on the highest completed level of education in 2007, which was converted to years of education. To identify a causal relationship between education and depressive symptoms we use an instrumental variables approach (Mendelian randomization, MR) with a genetic risk score as an instrument for years of education. The genetic risk score was based on 74 genetic variants, which were associated with years of education in a genome-wide association study (GWAS). Because the genetic variants are randomly assigned at conception, they induce exogenous variation in years of education and thus identify a causal effect if the assumptions of the MR approach are met. In Ordinary Least Squares (OLS) estimation years of education in 2007 were negatively associated with depressive symptoms in 2007 (b?=?-0.027, 95% Confidence Interval (CI)?=?-0.040, -0.015). However, the results based on Mendelian randomization suggested that the effect is not causal (b?=?0.017; 95% CI?=?-0.144, 0.178). This indicates that omitted variables correlated with education and depression may bias the linear regression coefficients and exogenous variation in education caused by differences in genetic make-up does not seem to protect against depressive symptoms.
PubMed ID
30145350 View in PubMed
Less detail

Aortic sinus diameter in middle age is associated with body size in young adulthood.

https://arctichealth.org/en/permalink/ahliterature297426
Source
Heart. 2018 05; 104(9):773-778
Publication Type
Journal Article
Date
05-2018
Author
Jussi A Hernesniemi
Jarkko Heiskanen
Saku Ruohonen
Noora Kartiosuo
Nina Hutri-Kähönen
Mika Kähönen
Eero Jokinen
Päivi Tossavainen
Merja Kallio
Tomi Laitinen
Terho Lehtimäki
Jorma S A Viikari
Markus Juonala
Olli T Raitakari
Author Affiliation
Department of Cardiology, Tays Heart Hospital, Tampere University Hospital and Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
Source
Heart. 2018 05; 104(9):773-778
Date
05-2018
Language
English
Publication Type
Journal Article
Keywords
Adolescent
Adult
Body Size - physiology
Body surface area
Child
Child, Preschool
Echocardiography
Female
Finland
Follow-Up Studies
Humans
Longitudinal Studies
Male
Middle Aged
Sinus of Valsalva - anatomy & histology
Young Adult
Abstract
Aortic sinus dilatation can lead to aortic valve regurgitation or even aortic dissection. Our objective was to examine the association between body surface area (BSA) measures from childhood to middle age and aortic sinus diameter in middle age. Understanding the relation of these two clarifies how aortic size is normally determined.
Cardiovascular Risk in Young Finns Study is a longitudinal study with follow-up of over 31 years (1980-2011). The study comprises information of body composition from multiple time points of 1950 subjects with cardiac ultrasound measurements made in 2011. The association between BSA in different ages and aortic sinus diameter in middle age was analysed by linear regression modelling adjusted with age, sex and diastolic blood pressure. Missing BSA values were derived for each life year (ages 3-33 years) from subject-specific curves for body weight and height estimated from longitudinal measurements using mixed model regression splines.
BSA estimates in early 20s are most strongly associated with aortic sinus diameter in middle age. Top association was observed at age 23 years with one SD increase in estimated BSA corresponding to 1.04?mm (0.87-1.21?mm) increase in aortic diameter. Increase in body weight beyond early 20s does not associate with aortic sinus diameter, and the association between middle age BSA and aortic size is substantially weaker (0.74?mm increase (0.58-0.89?mm)). These results were confirmed in a subpopulation using only measured data.
The association between aortic sinus diameter and BSA is stronger when considering BSA in young adulthood compared with BSA in middle age.
PubMed ID
29092920 View in PubMed
Less detail

Longitudinal associations of temperament and character with paranoid ideation: A population-based study.

https://arctichealth.org/en/permalink/ahliterature296020
Source
Psychiatry Res. 2018 03; 261:137-142
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
03-2018
Author
Aino Saarinen
Tom Rosenström
Mirka Hintsanen
Christian Hakulinen
Laura Pulkki-Råback
Terho Lehtimäki
Olli T Raitakari
Claude Robert Cloninger
Liisa Keltikangas-Järvinen
Author Affiliation
Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland.
Source
Psychiatry Res. 2018 03; 261:137-142
Date
03-2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Character
Female
Finland - epidemiology
Humans
Longitudinal Studies
Male
Paranoid Personality Disorder - diagnosis - epidemiology - psychology
Personality Inventory
Population Surveillance - methods
Prospective Studies
Risk factors
Temperament - physiology
Young Adult
Abstract
The aim of this study was to examine (a) the associations of temperament and character dimensions with paranoid ideation over a 15-year follow-up in the general population (b) the associations of explosive temperament and organized character profiles with paranoid ideation. 2137 subjects of the Young Finns Study completed the Temperament and Character Inventory and the Paranoid Ideation Scale of the Symptom Checklist-90 Revised in 1997, 2001, and 2012. Temperament dimensions of high novelty seeking, high harm avoidance, low reward dependence and explosive temperament profile were associated with the development of higher paranoid ideation. Regarding character, high self-directedness, high cooperativeness, and low self-transcendence and organized character profile were associated with lower paranoid ideation. These associations sustained after controlling for age, gender, and socioeconomic factors. However, the associations between temperament and paranoia mostly disappeared after taking character into account. Our study supported the hypothesis that personality dimensions contribute to the development of paranoid ideation. Temperament and character might combine a variety of single previously found risk factors into a more comprehensive framework for the developmental etiology of paranoia. Our findings provide evidence for psychotherapeutic interventions that support the self-regulation of temperamental vulnerabilities by internalizing mature concepts about the self and social relationships.
PubMed ID
29304427 View in PubMed
Less detail

The co-occurrence between depressive symptoms and paranoid ideation: A population-based longitudinal study.

https://arctichealth.org/en/permalink/ahliterature294814
Source
J Affect Disord. 2018 03 15; 229:48-55
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
03-15-2018
Author
Aino Saarinen
Mirka Hintsanen
Christian Hakulinen
Laura Pulkki-Råback
Terho Lehtimäki
Olli Raitakari
Liisa Keltikangas-Järvinen
Author Affiliation
Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland.
Source
J Affect Disord. 2018 03 15; 229:48-55
Date
03-15-2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Adult
Age Factors
Depression - psychology
Female
Finland
Humans
Longitudinal Studies
Male
Middle Aged
Paranoid Disorders - psychology
Psychiatric Status Rating Scales
Young Adult
Abstract
The aim of this study was to examine longitudinally in the general population (a) whether depressive symptoms co-occur with paranoid ideation from late adolescence to middle age (b) whether depressive subsymptoms are differently linked with paranoid ideation (c) whether depressive symptoms are associated with state-level or trait-level paranoid ideation.
Altogether 2109 subjects of the Young Finns study completed the Paranoid Ideation Scale of the Symptom Checklist-90 Revised and a modified version of the Beck Depression Inventory in 1992, 1997, 2001, 2007, and 2012, and the Beck Depression Inventory-II in 2007, 2011, and 2012.
Higher self-rated depressive symptoms were associated with the course of more severe paranoid ideation over age, especially in late adolescence and early adulthood. Regarding depressive subsymptoms, the associations of negative attitude and performance difficulties with paranoid ideation were evident over age, whereas the influence of somatic symptoms (such as changes in sleep and appetite) was not significant until after early adulthood. Additionally, depressive symptoms were more evidently associated with the development of trait- than state-level paranoid ideation.
Our study mostly captured mild depressive and paranoid symptoms. The results cannot be directly generalized to clinical populations.
Depressive symptoms were associated with the course of paranoid ideation from late adolescence to middle age. Patients with paranoid ideation might merit from evaluation of potential depressive symptoms, especially in late adolescence and early adulthood. Among patients with co-occurring depressive symptoms and paranoid ideation, there may be a substantial need for neurocognitive rehabilitation and community-based treatments.
PubMed ID
29306058 View in PubMed
Less detail

Fasting Glucose and the Risk of Depressive Symptoms: Instrumental-Variable Regression in the Cardiovascular Risk in Young Finns Study.

https://arctichealth.org/en/permalink/ahliterature292244
Source
Int J Behav Med. 2017 12; 24(6):901-907
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
12-2017
Author
Karolina Wesolowska
Marko Elovainio
Taina Hintsa
Markus Jokela
Laura Pulkki-Råback
Niina Pitkänen
Jari Lipsanen
Janne Tukiainen
Leo-Pekka Lyytikäinen
Terho Lehtimäki
Markus Juonala
Olli Raitakari
Liisa Keltikangas-Järvinen
Author Affiliation
Institute of Behavioral Sciences, University of Helsinki, P.O. Box 9, (Siltavuorenpenger 1 A), 00014, Helsinki, Finland. karolina.wesolowska@helsinki.fi.
Source
Int J Behav Med. 2017 12; 24(6):901-907
Date
12-2017
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Cardiovascular Diseases - etiology
Child
Child, Preschool
Cross-Sectional Studies
Depression - epidemiology
Diabetes Mellitus, Type 2 - diagnosis - psychology
Fasting
Female
Finland
Glucose
Humans
Male
Risk factors
Abstract
Type 2 diabetes (T2D) has been associated with depressive symptoms, but the causal direction of this association and the underlying mechanisms, such as increased glucose levels, remain unclear. We used instrumental-variable regression with a genetic instrument (Mendelian randomization) to examine a causal role of increased glucose concentrations in the development of depressive symptoms.
Data were from the population-based Cardiovascular Risk in Young Finns Study (n = 1217). Depressive symptoms were assessed in 2012 using a modified Beck Depression Inventory (BDI-I). Fasting glucose was measured concurrently with depressive symptoms. A genetic risk score for fasting glucose (with 35 single nucleotide polymorphisms) was used as an instrumental variable for glucose.
Glucose was not associated with depressive symptoms in the standard linear regression (B = -0.04, 95% CI [-0.12, 0.04], p = .34), but the instrumental-variable regression showed an inverse association between glucose and depressive symptoms (B = -0.43, 95% CI [-0.79, -0.07], p = .020). The difference between the estimates of standard linear regression and instrumental-variable regression was significant (p = .026) CONCLUSION: Our results suggest that the association between T2D and depressive symptoms is unlikely to be caused by increased glucose concentrations. It seems possible that T2D might be linked to depressive symptoms due to low glucose levels.
Notes
Cites: BMJ. 2005 Mar 26;330(7493):705-6 PMID 15684022
Cites: Int J Epidemiol. 2008 Dec;37(6):1220-6 PMID 18263651
Cites: BMJ. 2003 Dec 13;327(7428):1383-4 PMID 14670883
Cites: Diabet Med. 2008 Nov;25(11):1330-6 PMID 19046224
Cites: J Clin Epidemiol. 2002 Aug;55(8):767-73 PMID 12384190
Cites: Nat Methods. 2011 Dec 04;9(2):179-81 PMID 22138821
Cites: Am J Epidemiol. 2012 Feb 15;175(4):332-9 PMID 22247045
Cites: Diabetologia. 2010 Dec;53(12):2480-6 PMID 20711716
Cites: Diabetes Care. 2009 Oct;32(10):1867-9 PMID 19592627
Cites: Psychol Med. 2011 Sep;41(9):1889-96 PMID 21284915
Cites: Diabetes Res Clin Pract. 2012 Jun;96(3):313-8 PMID 22296853
Cites: Ann Med. 1991 Feb;23(1):35-9 PMID 2036203
Cites: J Affect Disord. 2004 Dec;83(2-3):227-32 PMID 15555718
Cites: PLoS One. 2012;7(11):e50841 PMID 23226400
Cites: Diabetes Care. 2004 May;27(5):1047-53 PMID 15111519
Cites: Diabetes Care. 2008 Dec;31(12):2383-90 PMID 19033418
Cites: Biol Psychiatry. 2010 Jan 15;67(2):189-92 PMID 19892320
Cites: Health Psychol. 2009 Jan;28(1):108-16 PMID 19210024
Cites: Int J Epidemiol. 2013 Aug;42(4):1134-44 PMID 24062299
Cites: Scand J Public Health. 2014 Nov;42(7):563-71 PMID 25053467
Cites: J Affect Disord. 2010 Jun;123(1-3):230-7 PMID 19896201
Cites: PLoS Med. 2007 Dec;4(12 ):e352 PMID 18076282
Cites: BMJ. 2005 Jan 1;330(7481):17-8 PMID 15604155
Cites: J Affect Disord. 2015 Jan 1;170:203-12 PMID 25254618
Cites: Nat Genet. 2012 May 13;44(6):659-69 PMID 22581228
Cites: Nat Genet. 2012 Sep;44(9):991-1005 PMID 22885924
Cites: Health Psychol. 2013 Mar;32(3):254-63 PMID 23437855
Cites: Am J Prev Med. 2005 Apr;28(3):267-73 PMID 15766614
Cites: Stat Med. 2008 Apr 15;27(8):1133-63 PMID 17886233
Cites: BMC Med Res Methodol. 2012 Feb 29;12:21 PMID 22375553
Cites: Soc Sci Med. 2006 Sep;63(5):1374-82 PMID 16709439
Cites: Diabetes Care. 2011 Mar;34(3):752-62 PMID 21357362
Cites: Diabetes Care. 2010 May;33(5):1128-33 PMID 20185745
Cites: Lancet Diabetes Endocrinol. 2014 Mar;2(3):236-45 PMID 24622754
Cites: Psychosom Med. 2005 Jul-Aug;67(4):561-7 PMID 16046368
Cites: Nature. 2010 Oct 28;467(7319):1061-73 PMID 20981092
Cites: BMJ Open Diabetes Res Care. 2015 May 16;3(1):e000063 PMID 26019877
Cites: Int J Epidemiol. 2004 Feb;33(1):30-42 PMID 15075143
Cites: PLoS Genet. 2009 Jun;5(6):e1000529 PMID 19543373
Cites: Nat Genet. 2010 Feb;42(2):105-16 PMID 20081858
Cites: JAMA. 2008 Jun 18;299(23):2751-9 PMID 18560002
PubMed ID
28779468 View in PubMed
Less detail

Blood hsa-miR-122-5p and hsa-miR-885-5p levels associate with fatty liver and related lipoprotein metabolism-The Young Finns Study.

https://arctichealth.org/en/permalink/ahliterature292130
Source
Sci Rep. 2016 12 05; 6:38262
Publication Type
Clinical Trial
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Date
12-05-2016
Author
Emma Raitoharju
Ilkka Seppälä
Leo-Pekka Lyytikäinen
Jorma Viikari
Mika Ala-Korpela
Pasi Soininen
Antti J Kangas
Melanie Waldenberger
Norman Klopp
Thomas Illig
Jaana Leiviskä
Britt-Marie Loo
Niku Oksala
Mika Kähönen
Nina Hutri-Kähönen
Reijo Laaksonen
Olli Raitakari
Terho Lehtimäki
Author Affiliation
Department of Clinical Chemistry, Pirkanmaa Hospital District, Fimlab Laboratories, and University of Tampere, School of Medicine, Tampere, Finland.
Source
Sci Rep. 2016 12 05; 6:38262
Date
12-05-2016
Language
English
Publication Type
Clinical Trial
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Adult
Child
Child, Preschool
Fatty Liver - blood - diagnostic imaging - epidemiology
Female
Finland - epidemiology
Follow-Up Studies
Genome-Wide Association Study
Humans
Lipoproteins - blood
Male
MicroRNAs - blood
Middle Aged
Ultrasonography
Abstract
MicroRNAs are involved in disease development and may be utilized as biomarkers. We investigated the association of blood miRNA levels and a) fatty liver (FL), b) lipoprotein and lipid pathways involved in liver lipid accumulation and c) levels of predicted mRNA targets in general population based cohort. Blood microRNA profiling (TaqMan OpenArray), genome-wide gene expression arrays and nuclear magnetic resonance metabolomics were performed for Young Finns Study participants aged 34-49 years (n?=?871). Liver fat status was assessed ultrasonographically. Levels of hsa-miR-122-5p and -885-5p were up-regulated in individuals with FL (fold change (FC)?=?1.55, p?=?1.36?*?10-14 and FC?=?1.25, p?=?4.86?*?10-4, respectively). In regression model adjusted with age, sex and BMI, hsa-miR-122-5p and -885-5p predicted FL (OR?=?2.07, p?=?1.29?*?10-8 and OR?=?1.41, p?=?0.002, respectively). Together hsa-miR-122-5p and -885-5p slightly improved the detection of FL beyond established risk factors. These miRNAs may be associated with FL formation through the regulation of lipoprotein metabolism as hsa-miR-122-5p levels associated with small VLDL, IDL, and large LDL lipoprotein subclass components, while hsa-miR-885-5p levels associated inversely with XL HDL cholesterol levels. Hsa-miR-885-5p levels correlated inversely with oxysterol-binding protein 2 (OSBPL2) expression (r?=?-0.143, p?=?1.00?*?10-4) and suppressing the expression of this lipid receptor and sterol transporter could link hsa-miR-885-5p with HDL cholesterol levels.
Notes
Cites: N Engl J Med. 2010 Sep 30;363(14 ):1341-50 PMID 20879883
Cites: Nat Cell Biol. 2011 Apr;13(4):423-33 PMID 21423178
Cites: Int J Epidemiol. 2008 Dec;37(6):1220-6 PMID 18263651
Cites: Ann Med. 2015 Feb;47(1):40-6 PMID 25333756
Cites: PLoS One. 2013;8(3):e58895 PMID 23516571
Cites: PLoS One. 2012;7(9):e45352 PMID 23028956
Cites: J Lipid Res. 2002 Feb;43(2):245-55 PMID 11861666
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: PLoS One. 2011 Jan 24;6(1):e16081 PMID 21283674
Cites: J Clin Invest. 2012 Aug;122(8):2871-83 PMID 22820288
Cites: Mol Syst Biol. 2010 Dec 21;6:441 PMID 21179014
Cites: Clin Toxicol (Phila). 2016;54(1):53-5 PMID 26574140
Cites: Clin Chim Acta. 2013 Sep 23;424:99-103 PMID 23727030
Cites: Nat Cell Biol. 2007 Jun;9(6):654-9 PMID 17486113
Cites: PLoS One. 2014 Aug 20;9(8):e105192 PMID 25141008
Cites: PLoS One. 2011;6(8):e23937 PMID 21886843
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: Nature. 2008 Apr 17;452(7189):896-9 PMID 18368051
Cites: Sci Rep. 2015 Oct 22;5:15501 PMID 26489516
Cites: Int J Mol Med. 2013 Mar;31(3):547-54 PMID 23337955
Cites: Nucleic Acids Res. 2008 Mar;36(4):1153-62 PMID 18158304
Cites: N Engl J Med. 2013 May 2;368(18):1685-94 PMID 23534542
Cites: RNA Biol. 2004 Jul;1(2):106-13 PMID 17179747
Cites: N Engl J Med. 2002 Apr 18;346(16):1221-31 PMID 11961152
Cites: Mol Cell Endocrinol. 2014 Jun 25;391(1-2):41-9 PMID 24784704
Cites: Arterioscler Thromb Vasc Biol. 2012 Mar;32(3):815-21 PMID 22223734
Cites: BMC Gastroenterol. 2006 Nov 02;6:33 PMID 17081293
Cites: J Clin Invest. 2012 Aug;122(8):2884-97 PMID 22820290
Cites: Arterioscler Thromb Vasc Biol. 2013 Feb;33(2):178-85 PMID 23325474
Cites: Nucleic Acids Res. 2010 Nov;38(20):7248-59 PMID 20615901
Cites: Hepatology. 2012 Nov;56(5):1946-57 PMID 22684891
Cites: J Lipid Res. 2009 Jul;50(7):1305-15 PMID 19224871
Cites: BMC Genomics. 2014 Jun 14;15:474 PMID 24928098
Cites: J Biol Chem. 2010 Jun 4;285(23):17442-52 PMID 20353945
Cites: Alcohol Clin Exp Res. 2013 Jan;37 Suppl 1:E59-69 PMID 22823254
Cites: FASEB J. 2013 Apr;27(4):1404-12 PMID 23271051
Cites: Br Med J (Clin Res Ed). 1986 Jan 4;292(6512):13-5 PMID 3080046
Cites: Nat Rev Gastroenterol Hepatol. 2013 Sep;10(9):542-52 PMID 23689081
Cites: Hepatology. 2003 May;37(5):1202-19 PMID 12717402
Cites: J Cell Mol Med. 2014 Feb;18(2):197-207 PMID 24400890
Cites: Oncogene. 2006 Apr 20;25(17):2537-45 PMID 16331254
Cites: J Psychiatr Res. 2015 Dec;71:120-5 PMID 26473696
Cites: Recent Pat Cardiovasc Drug Discov. 2009 Jun;4(2):109-18 PMID 19519553
Cites: Hepatology. 2008 Dec;48(6):1810-20 PMID 19030170
Cites: Nat Rev Gastroenterol Hepatol. 2011 Aug 09;8(9):491-501 PMID 21826088
Cites: Nucleic Acids Res. 2013 Jan;41(Database issue):D252-7 PMID 23193297
Cites: Gut. 2015 May;64(5):800-12 PMID 24973316
Cites: World J Hepatol. 2014 Aug 27;6(8):613-20 PMID 25232454
Cites: Obesity (Silver Spring). 2009 Dec;17 (12 ):2239-44 PMID 19461588
Cites: PLoS One. 2012;7(10):e48366 PMID 23152743
Cites: J Infect. 2015 Mar;70(3):273-87 PMID 25452043
Cites: Cell Metab. 2006 Feb;3(2):87-98 PMID 16459310
Cites: Eur J Cancer. 2013 Nov;49(16):3442-9 PMID 23810247
Cites: Cell. 2009 Jan 23;136(2):215-33 PMID 19167326
Cites: Biomed Res Int. 2014;2014:741465 PMID 24745023
Cites: Exp Mol Med. 2015 Sep 18;47:e184 PMID 26380927
Cites: Clin Sci (Lond). 2011 Mar;120(5):183-93 PMID 20815808
Cites: BMC Public Health. 2010 May 10;10:237 PMID 20459722
Cites: Eur Heart J. 2015 Oct 14;36(39):2635-42 PMID 26049157
Cites: Liver Int. 2013 Sep;33(8):1257-65 PMID 23682678
Cites: J Hepatol. 2015 Apr;62(1 Suppl):S47-64 PMID 25920090
PubMed ID
27917915 View in PubMed
Less detail

Blood pathway analyses reveal differences between prediabetic subjects with or without dyslipidaemia. The Cardiovascular Risk in Young Finns Study.

https://arctichealth.org/en/permalink/ahliterature292119
Source
Diabetes Metab Res Rev. 2017 10; 33(7):
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2017
Author
Jaakko Laaksonen
Tuukka Taipale
Ilkka Seppälä
Emma Raitoharju
Nina Mononen
Leo-Pekka Lyytikäinen
Melanie Waldenberger
Thomas Illig
Nina Hutri-Kähönen
Tapani Rönnemaa
Markus Juonala
Jorma Viikari
Mika Kähönen
Olli Raitakari
Terho Lehtimäki
Author Affiliation
Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center - Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland.
Source
Diabetes Metab Res Rev. 2017 10; 33(7):
Date
10-2017
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Blood glucose
Cardiovascular Diseases - etiology - genetics
Cholesterol - biosynthesis
Dyslipidemias - blood - complications - genetics
Female
Finland
Gene Expression
Gene Expression Profiling
Humans
Insulin Resistance
Male
Middle Aged
Prediabetic State - blood - complications - genetics
Signal Transduction - genetics
Up-Regulation
Waist Circumference
Abstract
Prediabetes often occurs together with dyslipidaemia, which is paradoxically treated with statins predisposing to type 2 diabetes mellitus. We examined peripheral blood pathway profiles in prediabetic subjects with (PRD ) and without dyslipidaemia (PR0 ) and compared these to nonprediabetic controls without dyslipidaemia (C0 ).
The participants were from the Cardiovascular Risk in Young Finns Study, including 1240 subjects aged 34 to 49 years. Genome-wide expression data of peripheral blood and gene set enrichment analysis were used to investigate the differentially expressed genes and enriched pathways between different subtypes of prediabetes.
Pathways for cholesterol synthesis, interleukin-12-mediated signalling events, and downstream signalling in naïve CD8+ T-cells were upregulated in the PR0 group in comparison with controls (C0 ). The upregulation of these pathways was independent of waist circumference, blood pressure, smoking status, and insulin. Adjustment for CRP left the CD8+ T-cell signalling and interleukin-12-mediated signalling event pathway upregulated. The cholesterol synthesis pathway was also upregulated when all prediabetic subjects (PR0 and PRD ) were compared with the nonprediabetic control group. No pathways were upregulated or downregulated when the PRD group was compared with the C0 group. Five genes in the PR0 group and 1 in the PRD group were significantly differentially expressed in comparison with the C0 group.
Blood cell gene expression profiles differ significantly between prediabetic subjects with and without dyslipidaemia. Whether this classification may be used in detection of prediabetic individuals at a high risk of cardiovascular complications remains to be examined.
PubMed ID
28609607 View in PubMed
Less detail

Gene-environment interactions between education and body mass: Evidence from the UK and Finland.

https://arctichealth.org/en/permalink/ahliterature292687
Source
Soc Sci Med. 2017 Dec; 195:12-16
Publication Type
Journal Article
Date
Dec-2017
Author
Vikesh Amin
Petri Böckerman
Jutta Viinikainen
Melissa C Smart
Yanchun Bao
Meena Kumari
Niina Pitkänen
Terho Lehtimäki
Olli Raitakari
Jaakko Pehkonen
Author Affiliation
Department of Economics, Central Michigan University, United States. Electronic address: amin1v@cmich.edu.
Source
Soc Sci Med. 2017 Dec; 195:12-16
Date
Dec-2017
Language
English
Publication Type
Journal Article
Keywords
Adult
Aged
Aged, 80 and over
Body mass index
Educational Status
Female
Finland
Gene-Environment Interaction
Genetic Predisposition to Disease
Humans
Longitudinal Studies
Male
Middle Aged
Overweight - genetics
United Kingdom
Abstract
More education is associated with a lower body mass index (BMI) and likelihood of being overweight. However, since a large proportion of the variation in body mass is due to genetic makeup, it has been hypothesized that education may moderate the genetic risk. We estimate main associations between (i) education, (ii) genetic risk, and (iii) interactions between education and genetic risk on BMI and the probability of being overweight in the UK and Finland. The estimates show that education is negatively associated with BMI and overweightness, and genetic risk is positively associated. However, the interactions between education and genetic risk are small and statistically insignificant.
PubMed ID
29102742 View in PubMed
Less detail

Does higher education protect against obesity? Evidence using Mendelian randomization.

https://arctichealth.org/en/permalink/ahliterature291998
Source
Prev Med. 2017 Aug; 101:195-198
Publication Type
Journal Article
Date
Aug-2017
Author
Petri Böckerman
Jutta Viinikainen
Laura Pulkki-Råback
Christian Hakulinen
Niina Pitkänen
Terho Lehtimäki
Jaakko Pehkonen
Olli T Raitakari
Author Affiliation
Turku School of Economics, Labour Institute for Economic Research, Helsinki, Finland; IZA, Bonn. Electronic address: petri.bockerman@labour.fi.
Source
Prev Med. 2017 Aug; 101:195-198
Date
Aug-2017
Language
English
Publication Type
Journal Article
Keywords
Adult
Body mass index
Body Weight - genetics
Educational Status
Female
Finland
Genome-Wide Association Study - methods
Humans
Male
Mendelian Randomization Analysis - methods
Obesity - genetics
Abstract
The aim of this explorative study was to examine the effect of education on obesity using Mendelian randomization.
Participants (N=2011) were from the on-going nationally representative Young Finns Study (YFS) that began in 1980 when six cohorts (aged 30, 33, 36, 39, 42 and 45 in 2007) were recruited. The average value of BMI (kg/m2) measurements in 2007 and 2011 and genetic information were linked to comprehensive register-based information on the years of education in 2007. We first used a linear regression (Ordinary Least Squares, OLS) to estimate the relationship between education and BMI. To identify a causal relationship, we exploited Mendelian randomization and used a genetic score as an instrument for education. The genetic score was based on 74 genetic variants that genome-wide association studies (GWASs) have found to be associated with the years of education. Because the genotypes are randomly assigned at conception, the instrument causes exogenous variation in the years of education and thus enables identification of causal effects.
The years of education in 2007 were associated with lower BMI in 2007/2011 (regression coefficient (b)=-0.22; 95% Confidence Intervals [CI]=-0.29, -0.14) according to the linear regression results. The results based on Mendelian randomization suggests that there may be a negative causal effect of education on BMI (b=-0.84; 95% CI=-1.77, 0.09).
The findings indicate that education could be a protective factor against obesity in advanced countries.
PubMed ID
28645627 View in PubMed
Less detail

Genetic endowments, parental resources and adult health: Evidence from the Young Finns Study.

https://arctichealth.org/en/permalink/ahliterature291758
Source
Soc Sci Med. 2017 09; 188:191-200
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
09-2017
Author
Jaakko Pehkonen
Jutta Viinikainen
Petri Böckerman
Terho Lehtimäki
Niina Pitkänen
Olli Raitakari
Author Affiliation
School of Business and Economics, University of Jyvaskyla, Finland. Electronic address: jaakko.k.pehkonen@jyu.fi.
Source
Soc Sci Med. 2017 09; 188:191-200
Date
09-2017
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Body mass index
Cholesterol, HDL - analysis - blood
Cholesterol, LDL - analysis - blood
Female
Finland
Food Quality
Genome-Wide Association Study
Health status
Humans
Income - statistics & numerical data
Longitudinal Studies
Male
Social Determinants of Health
Socioeconomic Factors
Sports - statistics & numerical data
Surveys and Questionnaires
Triglycerides - analysis - blood
Abstract
This paper uses longitudinal survey data linked to administrative registers to examine socioeconomic gradients in health, particularly whether the effects of genetic endowments interact with the socioeconomic resources of the parental household. We find that genetic risk scores contribute to adult health measured by biomarkers. This result is consistent with the findings from genome-wide association studies. Socioeconomic gradients in health differ based on biomarker and resource measures. Family education is negatively related to obesity and the waist-hip ratio, and family income is negatively related to low-density lipoprotein cholesterol and triglyceride levels. Parental resources do not modify the effects of genetic endowment on adult health. However, there is evidence for gene-family income interactions for triglyceride levels, particularly among women.
PubMed ID
28457598 View in PubMed
Less detail

Childhood predictors of adult fatty liver. The Cardiovascular Risk in Young Finns Study.

https://arctichealth.org/en/permalink/ahliterature291515
Source
J Hepatol. 2016 Oct; 65(4):784-790
Publication Type
Journal Article
Date
Oct-2016
Author
Emmi Suomela
Mervi Oikonen
Niina Pitkänen
Ari Ahola-Olli
Johanna Virtanen
Riitta Parkkola
Eero Jokinen
Tomi Laitinen
Nina Hutri-Kähönen
Mika Kähönen
Terho Lehtimäki
Leena Taittonen
Päivi Tossavainen
Antti Jula
Britt-Marie Loo
Vera Mikkilä
Risto Telama
Jorma S A Viikari
Markus Juonala
Olli T Raitakari
Author Affiliation
Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland. Electronic address: emkasu@utu.fi.
Source
J Hepatol. 2016 Oct; 65(4):784-790
Date
Oct-2016
Language
English
Publication Type
Journal Article
Keywords
Adolescent
Cardiovascular diseases
Child
Fatty liver
Finland
Genetic Predisposition to Disease
Humans
Lipase
Liver
Longitudinal Studies
Membrane Proteins
Polymorphism, Single Nucleotide
Risk factors
Abstract
Fatty liver is a potentially preventable cause of serious liver diseases. This longitudinal study aimed to identify childhood risk factors of fatty liver in adulthood in a population-based group of Finnish adults.
Study cohort included 2,042 individuals from the Cardiovascular Risk in Young Finns Study aged 3-18years at baseline in 1980. During the latest follow-up in 2011, the liver was scanned by ultrasound. In addition to physical and environmental factors related to fatty liver, we examined whether the genetic risk posed by a single nucleotide polymorphism in the patatin-like phospholipase domain-containing protein 3 gene (PNPLA3) (rs738409) strengthens prediction of adult fatty liver.
Independent childhood predictors of adult fatty liver were small for gestational age, (odds ratio=1.71, 95% confidence interval=1.07-2.72), variant in PNPLA3 (1.63, 1.29-2.07 per one risk allele), variant in the transmembrane 6 superfamily 2 gene (TM6SF2) (1.57, 1.08-2.30), BMI (1.30, 1.07-1.59 per standard deviation) and insulin (1.25, 1.05-1.49 per standard deviation). Childhood blood pressure, physical activity, C-reactive protein, smoking, serum lipid levels or parental lifestyle factors did not predict fatty liver. Risk assessment based on childhood age, sex, BMI, insulin levels, birth weight, TM6SF2 and PNPLA3 was superior in predicting fatty liver compared with the approach using only age, sex, BMI and insulin levels (C statistics, 0.725 vs. 0.749; p=0.002).
Childhood risk factors on the development of fatty liver were small for gestational age, high insulin and high BMI. Prediction of adult fatty liver was enhanced by taking into account genetic variants in PNPLA3 and TM6SF2 genes.
The increase in pediatric obesity emphasizes the importance of identification of children and adolescents at high risk of fatty liver in adulthood. We used data from the longitudinal Cardiovascular Risk in Young Finns Study to examine the associations of childhood (3-18years) risk variables with fatty liver assessed in adulthood at the age of 34-49years. The findings suggest that a multifactorial approach with both lifestyle and genetic factors included would improve early identification of children with a high risk of adult fatty liver.
PubMed ID
27235307 View in PubMed
Less detail

Positive Psychosocial Factors in Childhood Predicting Lower Risk for Adult Type 2 Diabetes: The Cardiovascular Risk in Young Finns Study, 1980-2012.

https://arctichealth.org/en/permalink/ahliterature291491
Source
Am J Prev Med. 2017 Jun; 52(6):e157-e164
Publication Type
Journal Article
Date
Jun-2017
Author
Laura Pulkki-Råback
Marko Elovainio
Christian Hakulinen
Jari Lipsanen
Laura D Kubzansky
Mirka Hintsanen
Kateryna Savelieva
Anna Serlachius
Costan G Magnussen
Matthew A Sabin
David P Burgner
Terho Lehtimäki
Eero Jokinen
Tapani Rönnemaa
Vera Mikkilä
Antti Jula
Nina Hutri-Kähönen
Jorma Viikari
Liisa Keltikangas-Järvinen
Olli Raitakari
Markus Juonala
Author Affiliation
Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, Finland; Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland. Electronic address: laura.pulkki-raback@helsinki.fi.
Source
Am J Prev Med. 2017 Jun; 52(6):e157-e164
Date
Jun-2017
Language
English
Publication Type
Journal Article
Keywords
Adolescent
Adult
Body mass index
Cardiovascular Diseases - etiology
Child
Child Behavior - psychology
Child, Preschool
Cohort Studies
Diabetes Mellitus, Type 2 - epidemiology - etiology
Female
Finland - epidemiology
Health Behavior
Humans
Longitudinal Studies
Male
Risk factors
Social Class
Surveys and Questionnaires
Abstract
Type 2 diabetes is a public health concern, but psychosocial factors that may protect against the disease are unknown. This study examines whether a positive psychosocial environment in childhood is associated with lower risk for Type 2 diabetes in adulthood or healthier glucose trajectories over the life course, and whether BMI mediates the associations.
A cohort of 3,596 Finnish children was followed into adulthood over 32 years. An overall positive psychosocial score, consisting of six subdomains, was measured at study baseline (1980). Relative risk ratios and multilevel growth curve modeling were used to examine associations of the psychosocial score with Type 2 diabetes (2012) and glucose trajectories (1986-2012). The mediating effect by BMI was examined using mediation analysis. The analyses were conducted between June 2015 and January 2016.
There was a 21% decrease in the rate of Type 2 diabetes (relative risk ratio, 0.79; 95% CI=0.66, 0.94) for each 1-SD increase in the positive psychosocial score after adjustment for childhood cardiovascular risk factors and dietary behaviors. Adult BMI mediated 52% and weight gain mediated 25% of the association. The growth curve model showed healthier glucose trajectories (age X psychosocial score interaction, b= -0.01; p=0.010) for participants with higher versus lower positive psychosocial score in childhood.
Positive psychosocial environment in childhood seems to have beneficial influences on the risk for Type 2 diabetes over the life span. RCTs will be required to see if interventions directed at early-life circumstances are warranted.
PubMed ID
28284747 View in PubMed
Less detail

Neighbourhood effects in health behaviours: a test of social causation with repeat-measurement longitudinal data.

https://arctichealth.org/en/permalink/ahliterature289731
Source
Eur J Public Health. 2016 06; 26(3):417-21
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
06-2016
Author
Jaakko Airaksinen
Christian Hakulinen
Laura Pulkki-Råback
Terho Lehtimäki
Olli T Raitakari
Liisa Keltikangas-Järvinen
Markus Jokela
Author Affiliation
Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland jaakko.airaksinen@helsinki.fi.
Source
Eur J Public Health. 2016 06; 26(3):417-21
Date
06-2016
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adolescent
Adult
Child
Child, Preschool
Cohort Studies
Female
Finland
Health Behavior
Humans
Longitudinal Studies
Male
Prospective Studies
Residence Characteristics - statistics & numerical data
Risk factors
Social Class
Social Environment
Socioeconomic Factors
Abstract
Neighbourhood characteristics have been associated with health behaviours of residents. We used longitudinal data to examine whether neighbourhood characteristics (level of urbanization and socioeconomic status) are related to within-individual variations in health behaviours (alcohol consumption, smoking, exercise and self-interest in health) as people live in different neighbourhoods over time.
Participants were from the Young Finns prospective cohort study (N = 3145) with four repeated measurement times (1992, 2001, 2007 and 2011/2012). Neighbourhood socioeconomic status and level of urbanization were measured on the level of municipality and zip code area. Within-individual (i.e. fixed-effect) regression was used to examine whether these associations were observed within individuals who lived in different neighbourhood in different measurement times.
People living in more urban zip code areas were more likely to smoke (b = 0.06; CI = 0.03-0.09) and drink alcohol (b = 0.11; CI = 0.08-0.14), and these associations were replicated in within-individual analysis-supporting social causation. Neighbourhood socioeconomic status and urbanization were associated with higher interest in maintaining personal health (b = 0.05; CI = 0.03-0.08 and b = 0.05; CI = 0.02-0.07, respectively), and these associations were also similar in within-individual analysis. Physical exercise was not associated with neighbourhood characteristics.
These data lend partial support for the hypothesis that neighbourhood differences influence people's health behaviours.
PubMed ID
26568621 View in PubMed
Less detail

Effects of hormonal contraception on systemic metabolism: cross-sectional and longitudinal evidence.

https://arctichealth.org/en/permalink/ahliterature289694
Source
Int J Epidemiol. 2016 10; 45(5):1445-1457
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2016
Author
Qin Wang
Peter Würtz
Kirsi Auro
Laure Morin-Papunen
Antti J Kangas
Pasi Soininen
Mika Tiainen
Tuulia Tynkkynen
Anni Joensuu
Aki S Havulinna
Kristiina Aalto
Marko Salmi
Stefan Blankenberg
Tanja Zeller
Jorma Viikari
Mika Kähönen
Terho Lehtimäki
Veikko Salomaa
Sirpa Jalkanen
Marjo-Riitta Järvelin
Markus Perola
Olli T Raitakari
Debbie A Lawlor
Johannes Kettunen
Mika Ala-Korpela
Author Affiliation
Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Oulu, Finland.
Source
Int J Epidemiol. 2016 10; 45(5):1445-1457
Date
10-2016
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Cholesterol, HDL - blood
Contraceptives, Oral, Hormonal - pharmacology
Cross-Sectional Studies
Cytokines - blood
Fatty Acids - blood
Female
Finland
Humans
Linear Models
Longitudinal Studies
Metabolome - drug effects
Metabolomics
Progestins - pharmacology
Risk factors
Triglycerides - blood
Young Adult
Abstract
Hormonal contraception is commonly used worldwide, but its systemic effects across lipoprotein subclasses, fatty acids, circulating metabolites and cytokines remain poorly understood.
A comprehensive molecular profile (75 metabolic measures and 37 cytokines) was measured for up to 5841 women (age range 24-49 years) from three population-based cohorts. Women using combined oral contraceptive pills (COCPs) or progestin-only contraceptives (POCs) were compared with those who did not use hormonal contraception. Metabolomics profiles were reassessed for 869 women after 6 years to uncover the metabolic effects of starting, stopping and persistently using hormonal contraception.
The comprehensive molecular profiling allowed multiple new findings on the metabolic associations with the use of COCPs. They were positively associated with lipoprotein subclasses, including all high-density lipoprotein (HDL) subclasses. The associations with fatty acids and amino acids were strong and variable in direction. COCP use was negatively associated with albumin and positively associated with creatinine and inflammatory markers, including glycoprotein acetyls and several growth factors and interleukins. Our findings also confirmed previous results e.g. for increased circulating triglycerides and HDL cholesterol. Starting COCPs caused similar metabolic changes to those observed cross-sectionally: the changes were maintained in consistent users and normalized in those who stopped using. In contrast, POCs were only weakly associated with metabolic and inflammatory markers. Results were consistent across all cohorts and for different COCP preparations and different types of POC delivery.
Use of COCPs causes widespread metabolic and inflammatory effects. However, persistent use does not appear to accumulate the effects over time and the metabolic perturbations are reversed upon discontinuation. POCs have little effect on systemic metabolism and inflammation.
Notes
Cites: J Obstet Gynaecol Can. 2009 Jul;31(7):627-40 PMID 19761636
Cites: Clin Sci. 1971 Oct;41(4):301-7 PMID 5097474
Cites: Int J Epidemiol. 2008 Dec;37(6):1220-6 PMID 18263651
Cites: Diabetes Care. 2013 Mar;36(3):648-55 PMID 23129134
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):13-24 PMID 23384742
Cites: Contraception. 2004 Nov;70(5):365-70 PMID 15504374
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: N Engl J Med. 2012 Jul 5;367(1):20-9 PMID 22762315
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: Mol Syst Biol. 2010 Dec 21;6:441 PMID 21179014
Cites: Proc Natl Acad Sci U S A. 2000 Feb 1;97(3):1242-6 PMID 10655515
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Science. 2016 Mar 11;351(6278):1166-71 PMID 26965621
Cites: Blood. 2004 Feb 1;103(3):927-33 PMID 14551147
Cites: J Am Coll Cardiol. 2013 Jan 29;61(4):427-36 PMID 23265341
Cites: Endocrine. 2015 Aug;49(3):820-7 PMID 25539793
Cites: Contraception. 2012 May;85(5):446-52 PMID 22078632
Cites: Contraception. 2003 Jun;67(6):423-9 PMID 12814810
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: BMJ. 2015 May 26;350:h2135 PMID 26013557
Cites: Circulation. 2015 Feb 3;131(5):451-8 PMID 25623155
Cites: PLoS One. 2012;7(6):e37815 PMID 22675492
Cites: Am J Clin Nutr. 2014 Sep;100(3):746-55 PMID 25057156
Cites: Circulation. 2010 Jun 8;121(22):2388-97 PMID 20497981
Cites: BMJ. 2013 Sep 12;347:f5298 PMID 24030561
Cites: Am J Clin Nutr. 1978 May;31(5):794-8 PMID 645627
Cites: J Thromb Haemost. 2013 Jan;11(1):203-5 PMID 23122048
Cites: Drugs. 2000 Oct;60(4):721-869 PMID 11085198
Cites: BMJ. 2014 Nov 18;349:g6330 PMID 25406188
Cites: Scand J Clin Lab Invest. 2009;69(2):168-74 PMID 18937150
Cites: Eur J Contracept Reprod Health Care. 2010 Dec;15 Suppl 2:S42-53 PMID 21091166
Cites: Nat Genet. 2012 Jan 29;44(3):269-76 PMID 22286219
Cites: Mol Syst Biol. 2008;4:167 PMID 18277383
Cites: Kidney Int. 2004 Aug;66(2):591-6 PMID 15253711
Cites: J Nutr. 2007 Jun;137(6 Suppl 1):1586S-1590S; discussion 1597S-1598S PMID 17513431
Cites: N Engl J Med. 1990 Nov 15;323(20):1375-81 PMID 2146499
Cites: N Engl J Med. 2012 Nov 29;367(22):2089-99 PMID 23126252
Cites: BMJ. 2011 Oct 25;343:d6423 PMID 22027398
Cites: Eur J Hum Genet. 2016 Feb;24(2):284-90 PMID 26014426
Cites: J Clin Endocrinol Metab. 1995 Jun;80(6):1816-21 PMID 7775629
Cites: Lancet. 2012 Aug 11;380(9841):572-80 PMID 22607825
Cites: Contraception. 2001 Jul;64(1):11-6 PMID 11535207
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Eur J Clin Pharmacol. 1996;50(3):179-84 PMID 8737756
Cites: Int J Epidemiol. 2015 Apr;44(2):623-37 PMID 26050255
Cites: J Thromb Haemost. 2006 Jan;4(1):77-82 PMID 16409455
Cites: Am J Transl Res. 2014 Oct 11;6(5):614-24 PMID 25360225
Cites: Life Sci. 1995;56(9):687-95 PMID 7869850
Cites: Epidemiol Rev. 2014;36:57-70 PMID 24025350
Cites: Am J Obstet Gynecol. 2008 Nov;199(5):529.e1-529.e10 PMID 18533124
Cites: J Clin Endocrinol Metab. 2007 Jun;92(6):2074-9 PMID 17374706
Cites: Circulation. 2015 Mar 3;131(9):774-85 PMID 25573147
Cites: Natl Health Stat Report. 2013 Feb 14;(62):1-15 PMID 24988816
Cites: Nat Commun. 2014 Aug 21;5:4708 PMID 25144627
Cites: N Engl J Med. 2008 Oct 30;359(18):1897-908 PMID 18971492
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):25-34 PMID 23384743
Cites: Ann Intern Med. 2014 Mar 18;160(6):398-406 PMID 24723079
Cites: Contraception. 2004 Feb;69(2):105-13 PMID 14759614
Cites: JAMA. 2009 Jul 1;302(1):37-48 PMID 19567438
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Feb;27(1):35-45 PMID 23384744
Cites: N Engl J Med. 2012 Jun 14;366(24):2257-66 PMID 22693997
Cites: Lancet. 2013 Jul 27;382(9889):339-52 PMID 23727170
Cites: Circulation. 2013 Jan 22;127(3):340-8 PMID 23258601
Cites: Diabetes. 2012 Jun;61(6):1372-80 PMID 22511205
Cites: Lancet. 2014 Aug 16;384(9943):626-35 PMID 25131982
Cites: Contraception. 2005 Feb;71(2):118-21 PMID 15707561
PubMed ID
27538888 View in PubMed
Less detail

Metabolic profiling of alcohol consumption in 9778 young adults.

https://arctichealth.org/en/permalink/ahliterature289696
Source
Int J Epidemiol. 2016 10; 45(5):1493-1506
Publication Type
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Date
10-2016
Author
Peter Würtz
Sarah Cook
Qin Wang
Mika Tiainen
Tuulia Tynkkynen
Antti J Kangas
Pasi Soininen
Jaana Laitinen
Jorma Viikari
Mika Kähönen
Terho Lehtimäki
Markus Perola
Stefan Blankenberg
Tanja Zeller
Satu Männistö
Veikko Salomaa
Marjo-Riitta Järvelin
Olli T Raitakari
Mika Ala-Korpela
David A Leon
Author Affiliation
Computational Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland peter.wurtz@computationalmedicine.fi.
Source
Int J Epidemiol. 2016 10; 45(5):1493-1506
Date
10-2016
Language
English
Publication Type
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Keywords
Adult
Alcohol Drinking - blood - metabolism
Amino Acids - blood
Biomarkers - blood
Body mass index
Cross-Sectional Studies
Fatty Acids - blood
Female
Finland
Humans
Linear Models
Lipoproteins - blood
Male
Metabolome
Metabolomics
Risk factors
Abstract
High alcohol consumption is a major cause of morbidity, yet alcohol is associated with both favourable and adverse effects on cardiometabolic risk markers. We aimed to characterize the associations of usual alcohol consumption with a comprehensive systemic metabolite profile in young adults.
Cross-sectional associations of alcohol intake with 86 metabolic measures were assessed for 9778 individuals from three population-based cohorts from Finland (age 24-45 years, 52% women). Metabolic changes associated with change in alcohol intake during 6-year follow-up were further examined for 1466 individuals. Alcohol intake was assessed by questionnaires. Circulating lipids, fatty acids and metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays.
Increased alcohol intake was associated with cardiometabolic risk markers across multiple metabolic pathways, including higher lipid concentrations in HDL subclasses and smaller LDL particle size, increased proportions of monounsaturated fatty acids and decreased proportion of omega-6 fatty acids, lower concentrations of glutamine and citrate (P?
Notes
Cites: Diabetes. 2012 Jul;61(7):1895-902 PMID 22553379
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Atherosclerosis. 2009 Jun;204(2):e93-8 PMID 19124122
Cites: Alcohol Clin Exp Res. 2013 Apr;37(4):575-86 PMID 23134229
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: Curr Atheroscler Rep. 2012 Apr;14(2):108-14 PMID 22350634
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Circulation. 2014 Oct 7;130(15):1245-53 PMID 25124495
Cites: Am J Epidemiol. 1997 Dec 15;146(12):1019-24 PMID 9420526
Cites: Nat Commun. 2016 Mar 23;7:11122 PMID 27005778
Cites: J Clin Endocrinol Metab. 2007 Jul;92(7):2559-66 PMID 17440017
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: Sci Rep. 2015 Sep 14;5:14005 PMID 26364564
Cites: Eur Heart J. 2012 Sep;33(18):2307-16 PMID 22450427
Cites: J Clin Invest. 2013 Sep;123(9):3678-84 PMID 23999442
Cites: Environ Health Prev Med. 2016 Jan;21(1):18-26 PMID 26459263
Cites: Proc Natl Acad Sci U S A. 2011 Dec 6;108(49):19611-6 PMID 22106302
Cites: Am J Epidemiol. 1996 Aug 15;144(4):325-34 PMID 8712189
Cites: BMJ. 2014 Jul 10;349:g4164 PMID 25011450
Cites: Circulation. 2009 Feb 24;119(7):931-9 PMID 19204302
Cites: N Engl J Med. 2012 Nov 29;367(22):2089-99 PMID 23126252
Cites: Int J Epidemiol. 2013 Dec;42(6):1772-90 PMID 24415611
Cites: Lancet. 2012 Aug 11;380(9841):572-80 PMID 22607825
Cites: J Am Coll Cardiol. 2007 Sep 11;50(11):1009-14 PMID 17825708
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Lancet. 2015 Dec 5;386(10010):2287-323 PMID 26364544
Cites: Ann Intern Med. 2015 Oct 20;163(8):569-79 PMID 26458258
Cites: J Mol Psychiatry. 2013 Aug 23;1(1):13 PMID 25408906
Cites: Circulation. 2012 May 8;125(18):2222-31 PMID 22496159
Cites: BMJ. 2011 Feb 22;342:d636 PMID 21343206
Cites: Circulation. 2015 Mar 3;131(9):774-85 PMID 25573147
Cites: Am J Clin Nutr. 2014 Jun;99(6):1470-8 PMID 24760976
Cites: Int J Epidemiol. 2010 Apr;39(2):504-18 PMID 19959603
Cites: Cell Metab. 2013 Jul 2;18(1):43-50 PMID 23770128
Cites: Diabetes. 2013 Oct;62(10):3589-98 PMID 23835345
Cites: Sci Rep. 2015 Dec 21;5:18422 PMID 26687910
Cites: Circulation. 2013 Sep 17;128(12):1310-24 PMID 23969696
Cites: Circ Cardiovasc Genet. 2009 Oct;2(5):507-14 PMID 20031627
Cites: Circulation. 2013 Jan 22;127(3):340-8 PMID 23258601
Cites: Transl Psychiatry. 2013 Jul 02;3:e276 PMID 23820610
PubMed ID
27494945 View in PubMed
Less detail

Metabolic signatures of birthweight in 18 288 adolescents and adults.

https://arctichealth.org/en/permalink/ahliterature289671
Source
Int J Epidemiol. 2016 10; 45(5):1539-1550
Publication Type
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Date
10-2016
Author
Peter Würtz
Qin Wang
Marjo Niironen
Tuulia Tynkkynen
Mika Tiainen
Fotios Drenos
Antti J Kangas
Pasi Soininen
Michael R Skilton
Kauko Heikkilä
Anneli Pouta
Mika Kähönen
Terho Lehtimäki
Richard J Rose
Eero Kajantie
Markus Perola
Jaakko Kaprio
Johan G Eriksson
Olli T Raitakari
Debbie A Lawlor
George Davey Smith
Marjo-Riitta Järvelin
Mika Ala-Korpela
Kirsi Auro
Author Affiliation
Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland peter.wurtz@computationalmedicine.fi.
Source
Int J Epidemiol. 2016 10; 45(5):1539-1550
Date
10-2016
Language
English
Publication Type
Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Keywords
Adiposity
Adolescent
Adult
Aged
Amino Acids - blood
Biomarkers - blood
Body mass index
Disease Susceptibility - blood - metabolism
Fatty Acids - blood
Female
Finland
Gestational Age
High-Throughput Screening Assays
Humans
Infant, Low Birth Weight - blood - metabolism
Infant, Newborn
Lipoproteins - blood
Male
Metabolomics
Middle Aged
Risk factors
United Kingdom
Young Adult
Abstract
Lower birthweight is associated with increased susceptibility to cardiometabolic diseases in adulthood, but the underlying molecular pathways are incompletely understood. We examined associations of birthweight with a comprehensive metabolic profile measured in adolescents and adults.
High-throughput nuclear magnetic resonance metabolomics and biochemical assays were used to quantify 87 circulating metabolic measures in seven cohorts from Finland and the UK, comprising altogether 18 288 individuals (mean age 26 years, range 15-75). Metabolic associations with birthweight were assessed by linear regression models adjusted for sex, gestational age and age at blood sampling. The metabolic associations with birthweight were compared with the corresponding associations with adult body mass index (BMI).
Lower birthweight adjusted for gestational age was adversely associated with cardiometabolic biomarkers, including lipoprotein subclasses, fatty acids, amino acids and markers of inflammation and impaired liver function (P
Notes
Cites: BMJ. 1989 Mar 4;298(6673):564-7 PMID 2495113
Cites: Int J Epidemiol. 2013 Feb;42(1):111-27 PMID 22507743
Cites: Diabetes. 2012 Jul;61(7):1895-902 PMID 22553379
Cites: PLoS Med. 2007 Aug;4(8):e263 PMID 17760500
Cites: Diabetes Care. 2013 Nov;36(11):3732-8 PMID 24026559
Cites: Diabetologia. 2011 Aug;54(8):2016-24 PMID 21487729
Cites: BMJ. 1998 Jul 25;317(7153):241-5 PMID 9677213
Cites: Int J Epidemiol. 2015 Apr;44(2):578-86 PMID 26016847
Cites: JAMA. 2004 Dec 8;292(22):2755-64 PMID 15585736
Cites: Am J Clin Nutr. 2011 Dec;94(6 Suppl):1799S-1802S PMID 21613556
Cites: Circ Cardiovasc Genet. 2015 Feb;8(1):192-206 PMID 25691689
Cites: Ann Epidemiol. 2006 Jan;16(1):19-25 PMID 16039874
Cites: Hum Mol Genet. 2012 Dec 15;21(24):5344-58 PMID 22956269
Cites: Nat Genet. 2013 Nov;45(11):1345-52 PMID 24097064
Cites: PLoS Med. 2014 Dec 09;11(12):e1001765 PMID 25490400
Cites: Pediatr Res. 2013 Apr;73(4 Pt 2):570-6 PMID 23314292
Cites: Pediatrics. 2003 May;111(5 Pt 1):1081-9 PMID 12728092
Cites: Int J Epidemiol. 2016 Oct;45(5):1493-1506 PMID 27494945
Cites: Am J Hum Genet. 2014 Feb 6;94(2):198-208 PMID 24462370
Cites: Analyst. 2009 Sep;134(9):1781-5 PMID 19684899
Cites: Int J Epidemiol. 2011 Jun;40(3):647-61 PMID 21324938
Cites: Diabetologia. 1998 Oct;41(10):1133-8 PMID 9794098
Cites: BMJ. 1996 Feb 17;312(7028):406-10 PMID 8601111
Cites: Am J Hum Biol. 2013 Jul-Aug;25(4):465-72 PMID 23649903
Cites: BMJ. 1995 Jul 15;311(6998):171-4 PMID 7613432
Cites: BMJ. 1999 Jul 24;319(7204):245-9 PMID 10417093
Cites: JAMA. 2008 Dec 24;300(24):2886-97 PMID 19109117
Cites: Diabetologia. 1992 Jul;35(7):595-601 PMID 1644236
Cites: Scand J Public Health. 2014 Nov;42(7):563-71 PMID 25053467
Cites: Lipids Health Dis. 2013 Apr 30;12:57 PMID 23631373
Cites: Am J Epidemiol. 2007 Sep 15;166(6):634-45 PMID 17456478
Cites: Eur Heart J. 2008 Apr;29(8):1049-56 PMID 18403494
Cites: Proc Natl Acad Sci U S A. 2013 Jan 29;110(5):1917-22 PMID 23277558
Cites: BMJ. 1995 Feb 18;310(6977):432-6 PMID 7873948
Cites: Hypertension. 2004 Dec;44(6):838-46 PMID 15520301
Cites: PLoS Med. 2014 Feb 25;11(2):e1001606 PMID 24586121
Cites: Nat Med. 2011 Apr;17(4):448-53 PMID 21423183
Cites: JAMA. 2016 Mar 15;315(11):1129-40 PMID 26978208
Cites: Lancet. 2002 Aug 31;360(9334):659-65 PMID 12241871
Cites: PLoS Med. 2012;9(5):e1001212 PMID 22563304
Cites: Int J Epidemiol. 2003 Oct;32(5):862-76 PMID 14559765
Cites: N Engl J Med. 2008 Jul 3;359(1):61-73 PMID 18596274
Cites: Circulation. 2015 Mar 3;131(9):774-85 PMID 25573147
Cites: Diabetes Care. 2009 Apr;32(4):741-50 PMID 19131466
Cites: PLoS One. 2016 Feb 10;11(2):e0148361 PMID 26863521
Cites: Nat Genet. 2013 Jan;45(1):76-82 PMID 23202124
Cites: Nat Commun. 2014 Aug 21;5:4708 PMID 25144627
Cites: Biomed Res Int. 2013;2013:720514 PMID 23841090
Cites: BMJ. 1996 Feb 17;312(7028):401-6 PMID 8601110
Cites: Am J Clin Nutr. 2007 May;85(5):1244-50 PMID 17490959
Cites: PLoS Med. 2013;10 (6):e1001474 PMID 23824655
Cites: Nature. 2004 Jan 29;427(6973):411-2 PMID 14749819
Cites: Nutrition. 2016 Jul-Aug;32(7-8):725-31 PMID 27025974
Cites: Int J Epidemiol. 2002 Dec;31(6):1235-9 PMID 12540728
Cites: Nature. 2004 Jul 22;430(6998):419-21 PMID 15269759
Cites: Diabetologia. 2015 May;58(5):968-79 PMID 25693751
PubMed ID
27892411 View in PubMed
Less detail

41 records – page 1 of 3.